{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9bcf26ec-fd27-4d2b-aa31-cb20e3e3f6b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.head of           id    ts_code  trade_date    the_date  opens   high    low  closes  \\\n",
      "0    1864378  000001.SZ  2023-01-03  2023-01-03  13.20  13.85  13.05   13.77   \n",
      "1    1861112  000001.SZ  2023-01-04  2023-01-04  13.71  14.42  13.63   14.32   \n",
      "2    1853415  000001.SZ  2023-01-05  2023-01-05  14.40  14.74  14.37   14.48   \n",
      "3    1852119  000001.SZ  2023-01-06  2023-01-06  14.50  14.72  14.48   14.62   \n",
      "4    1846912  000001.SZ  2023-01-09  2023-01-09  14.75  14.88  14.52   14.80   \n",
      "..       ...        ...         ...         ...    ...    ...    ...     ...   \n",
      "237   632401  000001.SZ  2023-12-25  2023-12-25   9.18   9.20   9.14    9.19   \n",
      "238   630017  000001.SZ  2023-12-26  2023-12-26   9.19   9.20   9.07    9.10   \n",
      "239   624108  000001.SZ  2023-12-27  2023-12-27   9.10   9.13   9.02    9.12   \n",
      "240   618845  000001.SZ  2023-12-28  2023-12-28   9.11   9.47   9.08    9.45   \n",
      "241   610390  000001.SZ  2023-12-29  2023-12-29   9.42   9.48   9.35    9.39   \n",
      "\n",
      "     pre_closes  changes  pct_chg        vol     amount  zd_closes  \n",
      "0         13.16     0.61   4.6353  2194130.0  2971550.0        NaN  \n",
      "1         13.77     0.55   3.9942  2189680.0  3110730.0       0.04  \n",
      "2         14.32     0.16   1.1173  1665430.0  2417270.0       0.01  \n",
      "3         14.48     0.14   0.9669  1195740.0  1747920.0       0.01  \n",
      "4         14.62     0.18   1.2312  1057660.0  1561370.0       0.01  \n",
      "..          ...      ...      ...        ...        ...        ...  \n",
      "237        9.20    -0.01  -0.1087   413971.0   379638.0      -0.00  \n",
      "238        9.19    -0.09  -0.9793   541896.0   493747.0      -0.01  \n",
      "239        9.10     0.02   0.2198   641534.0   582037.0       0.00  \n",
      "240        9.12     0.33   3.6184  1661590.0  1550260.0       0.04  \n",
      "241        9.45    -0.06  -0.6349   853853.0   803197.0      -0.01  \n",
      "\n",
      "[242 rows x 14 columns]>\n",
      "<bound method NDFrame.head of           id    ts_code  trade_date    the_date  opens   high    low  closes  \\\n",
      "1    1861112  000001.SZ  2023-01-04  2023-01-04  13.71  14.42  13.63   14.32   \n",
      "2    1853415  000001.SZ  2023-01-05  2023-01-05  14.40  14.74  14.37   14.48   \n",
      "3    1852119  000001.SZ  2023-01-06  2023-01-06  14.50  14.72  14.48   14.62   \n",
      "4    1846912  000001.SZ  2023-01-09  2023-01-09  14.75  14.88  14.52   14.80   \n",
      "5    1841780  000001.SZ  2023-01-10  2023-01-10  14.76  14.89  14.39   14.44   \n",
      "..       ...        ...         ...         ...    ...    ...    ...     ...   \n",
      "237   632401  000001.SZ  2023-12-25  2023-12-25   9.18   9.20   9.14    9.19   \n",
      "238   630017  000001.SZ  2023-12-26  2023-12-26   9.19   9.20   9.07    9.10   \n",
      "239   624108  000001.SZ  2023-12-27  2023-12-27   9.10   9.13   9.02    9.12   \n",
      "240   618845  000001.SZ  2023-12-28  2023-12-28   9.11   9.47   9.08    9.45   \n",
      "241   610390  000001.SZ  2023-12-29  2023-12-29   9.42   9.48   9.35    9.39   \n",
      "\n",
      "     pre_closes  changes  pct_chg        vol     amount  zd_closes  \n",
      "1         13.77     0.55   3.9942  2189680.0  3110730.0       0.04  \n",
      "2         14.32     0.16   1.1173  1665430.0  2417270.0       0.01  \n",
      "3         14.48     0.14   0.9669  1195740.0  1747920.0       0.01  \n",
      "4         14.62     0.18   1.2312  1057660.0  1561370.0       0.01  \n",
      "5         14.80    -0.36  -2.4324  1269420.0  1851090.0      -0.02  \n",
      "..          ...      ...      ...        ...        ...        ...  \n",
      "237        9.20    -0.01  -0.1087   413971.0   379638.0      -0.00  \n",
      "238        9.19    -0.09  -0.9793   541896.0   493747.0      -0.01  \n",
      "239        9.10     0.02   0.2198   641534.0   582037.0       0.00  \n",
      "240        9.12     0.33   3.6184  1661590.0  1550260.0       0.04  \n",
      "241        9.45    -0.06  -0.6349   853853.0   803197.0      -0.01  \n",
      "\n",
      "[241 rows x 14 columns]>\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              zd_closes   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 3.711e+29\n",
      "Date:                Sat, 29 Mar 2025   Prob (F-statistic):               0.00\n",
      "Time:                        16:28:45   Log-Likelihood:                 8325.1\n",
      "No. Observations:                 241   AIC:                        -1.664e+04\n",
      "Df Residuals:                     238   BIC:                        -1.663e+04\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   4.933e-18   4.09e-17      0.121      0.904   -7.55e-17    8.54e-17\n",
      "vol         2.385e-22    4.1e-23      5.825      0.000    1.58e-22    3.19e-22\n",
      "zd_closes      1.0000   1.19e-15   8.38e+14      0.000       1.000       1.000\n",
      "==============================================================================\n",
      "Omnibus:                       82.674   Durbin-Watson:                   0.153\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              201.592\n",
      "Skew:                          -1.602   Prob(JB):                     1.68e-44\n",
      "Kurtosis:                       6.133   Cond. No.                     7.57e+07\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.57e+07. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhaun\\AppData\\Local\\Temp\\ipykernel_3172\\4004750880.py:28: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql_query(\n"
     ]
    }
   ],
   "source": [
    "# 导入包\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sqlalchemy import create_engine\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from matplotlib import pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pymysql\n",
    "\n",
    "db_config = {\n",
    "    'host': '111.231.14.211',\n",
    "    'user': 'tushare',\n",
    "    'password': 'root',\n",
    "    'database': 'tushare',\n",
    "    'port': 13307,          # 明确指定端口\n",
    "    'charset': 'utf8mb4'   # 添加字符集设置\n",
    "}\n",
    "engine = create_engine(\n",
    "    f\"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}?charset={db_config['charset']}\"\n",
    ")\n",
    "conn = pymysql.connect(**db_config)\n",
    "chunk_size = 10000\n",
    "\n",
    "# 获取华夏银行日线数据\n",
    "df = pd.read_sql_query(\n",
    "        \"\"\"\n",
    "        SELECT d.*\n",
    "        FROM date_1 d\n",
    "        WHERE d.trade_date BETWEEN '2023-01-01' AND '2023-12-31' AND d.ts_code='000001.SZ'\n",
    "        \"\"\", \n",
    "        conn, \n",
    "        chunksize=chunk_size\n",
    "    )\n",
    "df1 = pd.concat(df, ignore_index=True)\n",
    "\n",
    "df1['zd_closes'] = round(((df1['closes'] - df1['closes'].shift(1)) / df1['closes'].shift(1)), 2)\n",
    "print(df1.head)\n",
    "\n",
    "# 处理缺失数据\n",
    "df1 = df1.dropna(subset=['zd_closes'])\n",
    "print(df1.head)\n",
    "\n",
    "ex = [ 'id', 'ts_code', 'trade_date', 'the_date', 'opens', 'high', 'low', 'closes', 'pre_closes', 'changes', 'pct_chg', 'amount']\n",
    "number = df1.select_dtypes(include=['number']).columns.to_list()\n",
    "newList = [col for col in number if col not in ex]\n",
    "\n",
    "formuls = 'zd_closes ~ ' + ' + ' .join(newList)\n",
    "res = smf.ols(formuls, data=df1).fit()\n",
    "print(res.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9e47aca1-f1c1-4961-a005-a2fc1eebc13e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhaun\\AppData\\Local\\Temp\\ipykernel_3172\\2264675307.py:28: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql_query(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method NDFrame.head of           id    ts_code  trade_date    the_date  opens   high    low  closes  \\\n",
      "0    1863840  002229.SZ  2023-01-03  2023-01-03   6.65   7.16   6.57    6.94   \n",
      "1    1859929  002229.SZ  2023-01-04  2023-01-04   7.00   7.50   6.99    7.08   \n",
      "2    1856097  002229.SZ  2023-01-05  2023-01-05   7.08   7.24   6.97    7.07   \n",
      "3    1848745  002229.SZ  2023-01-06  2023-01-06   7.04   7.10   6.90    6.92   \n",
      "4    1846389  002229.SZ  2023-01-09  2023-01-09   6.91   6.99   6.84    6.88   \n",
      "..       ...        ...         ...         ...    ...    ...    ...     ...   \n",
      "237   633550  002229.SZ  2023-12-25  2023-12-25  26.57  27.25  25.89   27.06   \n",
      "238   629028  002229.SZ  2023-12-26  2023-12-26  27.10  27.10  26.36   26.52   \n",
      "239   620612  002229.SZ  2023-12-27  2023-12-27  26.52  27.98  26.40   27.52   \n",
      "240   617197  002229.SZ  2023-12-28  2023-12-28  27.45  28.18  27.09   27.82   \n",
      "241   610843  002229.SZ  2023-12-29  2023-12-29  27.81  28.36  27.68   27.80   \n",
      "\n",
      "     pre_closes  changes  ...  buy_lg_vol  sell_lg_vol  buy_elg_vol  \\\n",
      "0          6.65     0.29  ...       29987        18952          318   \n",
      "1          6.94     0.14  ...       23744        27135         5015   \n",
      "2          7.08    -0.01  ...       25158        21339            0   \n",
      "3          7.07    -0.15  ...       14349        20138            0   \n",
      "4          6.92    -0.04  ...       11518        13355         1940   \n",
      "..          ...      ...  ...         ...          ...          ...   \n",
      "237       26.78     0.28  ...       63271        65539        14730   \n",
      "238       27.06    -0.54  ...       34152        40973         9583   \n",
      "239       26.52     1.00  ...       92557        88908        19792   \n",
      "240       27.52     0.30  ...       81973        77325        21681   \n",
      "241       27.82    -0.02  ...       71775        84079        18064   \n",
      "\n",
      "     sell_elg_vol  net_mf_vol        i_vol  i_closes  zd_closes  zs_closes  \\\n",
      "0            1223        -249  281370000.0   3116.51        NaN        NaN   \n",
      "1            6598         589  273314000.0   3123.52       0.02       0.00   \n",
      "2            5487       -7236  257003000.0   3155.22      -0.00       0.01   \n",
      "3            7630       -5602  257380000.0   3157.64      -0.02       0.00   \n",
      "4             259       -7936  258115000.0   3176.08      -0.01       0.01   \n",
      "..            ...         ...          ...       ...        ...        ...   \n",
      "237         16395       19854  229814000.0   2918.81       0.01       0.00   \n",
      "238         19496      -19173  228141000.0   2898.88      -0.02      -0.01   \n",
      "239         30632        6225  247901000.0   2914.61       0.04       0.01   \n",
      "240         20878      -17659  339213000.0   2954.70       0.01       0.01   \n",
      "241         47224      -11063  290673000.0   2974.93      -0.00       0.01   \n",
      "\n",
      "     zs_vol  \n",
      "0       NaN  \n",
      "1     -0.03  \n",
      "2     -0.06  \n",
      "3      0.00  \n",
      "4      0.00  \n",
      "..      ...  \n",
      "237   -0.23  \n",
      "238   -0.01  \n",
      "239    0.09  \n",
      "240    0.37  \n",
      "241   -0.14  \n",
      "\n",
      "[242 rows x 23 columns]>\n",
      "<bound method NDFrame.head of           id    ts_code  trade_date    the_date  opens   high    low  closes  \\\n",
      "1    1859929  002229.SZ  2023-01-04  2023-01-04   7.00   7.50   6.99    7.08   \n",
      "2    1856097  002229.SZ  2023-01-05  2023-01-05   7.08   7.24   6.97    7.07   \n",
      "3    1848745  002229.SZ  2023-01-06  2023-01-06   7.04   7.10   6.90    6.92   \n",
      "4    1846389  002229.SZ  2023-01-09  2023-01-09   6.91   6.99   6.84    6.88   \n",
      "5    1841245  002229.SZ  2023-01-10  2023-01-10   6.88   6.90   6.76    6.80   \n",
      "..       ...        ...         ...         ...    ...    ...    ...     ...   \n",
      "237   633550  002229.SZ  2023-12-25  2023-12-25  26.57  27.25  25.89   27.06   \n",
      "238   629028  002229.SZ  2023-12-26  2023-12-26  27.10  27.10  26.36   26.52   \n",
      "239   620612  002229.SZ  2023-12-27  2023-12-27  26.52  27.98  26.40   27.52   \n",
      "240   617197  002229.SZ  2023-12-28  2023-12-28  27.45  28.18  27.09   27.82   \n",
      "241   610843  002229.SZ  2023-12-29  2023-12-29  27.81  28.36  27.68   27.80   \n",
      "\n",
      "     pre_closes  changes  ...  buy_lg_vol  sell_lg_vol  buy_elg_vol  \\\n",
      "1          6.94     0.14  ...       23744        27135         5015   \n",
      "2          7.08    -0.01  ...       25158        21339            0   \n",
      "3          7.07    -0.15  ...       14349        20138            0   \n",
      "4          6.92    -0.04  ...       11518        13355         1940   \n",
      "5          6.88    -0.08  ...       10259        14738         2993   \n",
      "..          ...      ...  ...         ...          ...          ...   \n",
      "237       26.78     0.28  ...       63271        65539        14730   \n",
      "238       27.06    -0.54  ...       34152        40973         9583   \n",
      "239       26.52     1.00  ...       92557        88908        19792   \n",
      "240       27.52     0.30  ...       81973        77325        21681   \n",
      "241       27.82    -0.02  ...       71775        84079        18064   \n",
      "\n",
      "     sell_elg_vol  net_mf_vol        i_vol  i_closes  zd_closes  zs_closes  \\\n",
      "1            6598         589  273314000.0   3123.52       0.02       0.00   \n",
      "2            5487       -7236  257003000.0   3155.22      -0.00       0.01   \n",
      "3            7630       -5602  257380000.0   3157.64      -0.02       0.00   \n",
      "4             259       -7936  258115000.0   3176.08      -0.01       0.01   \n",
      "5               0       -5884  232984000.0   3169.51      -0.01      -0.00   \n",
      "..            ...         ...          ...       ...        ...        ...   \n",
      "237         16395       19854  229814000.0   2918.81       0.01       0.00   \n",
      "238         19496      -19173  228141000.0   2898.88      -0.02      -0.01   \n",
      "239         30632        6225  247901000.0   2914.61       0.04       0.01   \n",
      "240         20878      -17659  339213000.0   2954.70       0.01       0.01   \n",
      "241         47224      -11063  290673000.0   2974.93      -0.00       0.01   \n",
      "\n",
      "     zs_vol  \n",
      "1     -0.03  \n",
      "2     -0.06  \n",
      "3      0.00  \n",
      "4      0.00  \n",
      "5     -0.10  \n",
      "..      ...  \n",
      "237   -0.23  \n",
      "238   -0.01  \n",
      "239    0.09  \n",
      "240    0.37  \n",
      "241   -0.14  \n",
      "\n",
      "[241 rows x 23 columns]>\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:              zd_closes   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 6.831e+27\n",
      "Date:                Sat, 29 Mar 2025   Prob (F-statistic):               0.00\n",
      "Time:                        16:29:04   Log-Likelihood:                 7735.3\n",
      "No. Observations:                 241   AIC:                        -1.544e+04\n",
      "Df Residuals:                     228   BIC:                        -1.540e+04\n",
      "Df Model:                          12                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept     4.441e-16   6.46e-15      0.069      0.945   -1.23e-14    1.32e-14\n",
      "vol           3.507e-21   3.94e-21      0.889      0.375   -4.26e-21    1.13e-20\n",
      "amount         2.87e-22   2.32e-22      1.239      0.217    -1.7e-22    7.44e-22\n",
      "buy_lg_vol   -2.008e-20   1.33e-20     -1.508      0.133   -4.63e-20    6.16e-21\n",
      "sell_lg_vol   3.825e-21   1.02e-20      0.374      0.709   -1.63e-20     2.4e-20\n",
      "buy_elg_vol  -1.241e-21   4.64e-21     -0.267      0.789   -1.04e-20     7.9e-21\n",
      "sell_elg_vol  3.461e-21   4.49e-21      0.771      0.442   -5.39e-21    1.23e-20\n",
      "net_mf_vol    7.108e-21   3.03e-21      2.344      0.020    1.13e-21    1.31e-20\n",
      "i_vol        -1.045e-23      4e-24     -2.612      0.010   -1.83e-23   -2.57e-24\n",
      "i_closes     -1.084e-19   2.14e-18     -0.051      0.960   -4.33e-18    4.12e-18\n",
      "zd_closes        1.0000   6.54e-15   1.53e+14      0.000       1.000       1.000\n",
      "zs_closes    -1.943e-16   2.58e-14     -0.008      0.994   -5.11e-14    5.07e-14\n",
      "zs_vol        8.609e-17   1.59e-15      0.054      0.957   -3.05e-15    3.22e-15\n",
      "==============================================================================\n",
      "Omnibus:                       30.059   Durbin-Watson:                   0.091\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               54.114\n",
      "Skew:                           0.675   Prob(JB):                     1.78e-12\n",
      "Kurtosis:                       4.888   Cond. No.                     4.30e+10\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.3e+10. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入包\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sqlalchemy import create_engine\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from matplotlib import pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pymysql\n",
    "\n",
    "db_config = {\n",
    "    'host': '111.231.14.211',\n",
    "    'user': 'tushare',\n",
    "    'password': 'root',\n",
    "    'database': 'tushare',\n",
    "    'port': 13307,          # 明确指定端口\n",
    "    'charset': 'utf8mb4'   # 添加字符集设置\n",
    "}\n",
    "engine = create_engine(\n",
    "    f\"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}?charset={db_config['charset']}\"\n",
    ")\n",
    "conn = pymysql.connect(**db_config)\n",
    "chunk_size = 10000\n",
    "\n",
    "# 获取华夏银行日线数据\n",
    "df = pd.read_sql_query(\n",
    "        \"\"\"\n",
    "        SELECT d.*, m.buy_lg_vol, m.sell_lg_vol, m.buy_elg_vol, m.sell_elg_vol, m.net_mf_vol,  i.vol as i_vol, i.closes as i_closes\n",
    "        FROM date_1 d\n",
    "        join moneyflows m on d.ts_code = m.ts_code and d.trade_date = m.trade_date\n",
    "        left join index_daily i on d.trade_date = i.trade_date and i.ts_code = '000001.SH'\n",
    "        WHERE d.trade_date BETWEEN '2023-01-01' AND '2023-12-31' AND d.ts_code='002229.SZ'\n",
    "        \"\"\", \n",
    "        conn, \n",
    "        chunksize=chunk_size\n",
    "    )\n",
    "df1 = pd.concat(df, ignore_index=True)\n",
    "\n",
    "df1['zd_closes'] = round(((df1['closes'] - df1['closes'].shift(1)) / df1['closes'].shift(1)), 2)\n",
    "df1['zs_closes'] = round(((df1['i_closes'] - df1['i_closes'].shift(1)) / df1['i_closes'].shift(1)), 2)\n",
    "df1['zs_vol'] = round(((df1['i_vol'] - df1['i_vol'].shift(1)) / df1['i_vol'].shift(1)), 2)\n",
    "print(df1.head)\n",
    "\n",
    "# 处理缺失数据\n",
    "df1 = df1.dropna(subset=['zd_closes', 'zs_closes', 'zs_vol'])\n",
    "print(df1.head)\n",
    "\n",
    "ex = [ 'id', 'ts_code', 'trade_date', 'the_date', 'opens', 'high', 'low', 'closes', 'pre_closes', 'changes', 'pct_chg']\n",
    "number = df1.select_dtypes(include=['number']).columns.to_list()\n",
    "newList = [col for col in number if col not in ex]\n",
    "\n",
    "formuls = 'zd_closes ~ ' + ' + ' .join(newList)\n",
    "res = smf.ols(formuls, data=df1).fit()\n",
    "print(res.summary())\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(res.fittedvalues, df1['zd_closes'])\n",
    "plt.show()\n",
    "features = df1[['vol', 'amount', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol', 'net_mf_vol']]\n",
    "correlation_matrix = features.corr()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', cbar=True, fmt='.2f', linewidths=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "de33bbb9-9e17-489c-84b1-a16962103ad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhaun\\AppData\\Local\\Temp\\ipykernel_3172\\2333566582.py:27: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql_query(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "累计贡献率为： 99.92 %\n",
      "\n",
      "回归模型结果:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               zd_close   R-squared:                       0.708\n",
      "Model:                            OLS   Adj. R-squared:                  0.693\n",
      "Method:                 Least Squares   F-statistic:                     46.12\n",
      "Date:                Sat, 29 Mar 2025   Prob (F-statistic):           6.01e-24\n",
      "Time:                        16:29:16   Log-Likelihood:                 199.52\n",
      "No. Observations:                 101   AIC:                            -387.0\n",
      "Df Residuals:                      95   BIC:                            -371.4\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0548      0.010      5.732      0.000       0.036       0.074\n",
      "PC1          5.66e-09   2.61e-09      2.171      0.032    4.85e-10    1.08e-08\n",
      "PC2         1.277e-08   7.86e-09      1.624      0.108   -2.84e-09    2.84e-08\n",
      "PC3         2.063e-07   2.16e-08      9.535      0.000    1.63e-07    2.49e-07\n",
      "PC4         4.626e-07   4.69e-08      9.863      0.000    3.69e-07    5.56e-07\n",
      "PC5          -6.4e-07   1.08e-07     -5.920      0.000   -8.55e-07   -4.25e-07\n",
      "==============================================================================\n",
      "Omnibus:                        0.550   Durbin-Watson:                   2.047\n",
      "Prob(Omnibus):                  0.760   Jarque-Bera (JB):                0.186\n",
      "Skew:                          -0.038   Prob(JB):                        0.911\n",
      "Kurtosis:                       3.196   Cond. No.                     8.20e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 8.2e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "\n",
      "回归模型结果:\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               zd_close   R-squared:                       0.700\n",
      "Model:                            OLS   Adj. R-squared:                  0.688\n",
      "Method:                 Least Squares   F-statistic:                     56.03\n",
      "Date:                Sat, 29 Mar 2025   Prob (F-statistic):           2.71e-24\n",
      "Time:                        16:29:16   Log-Likelihood:                 198.14\n",
      "No. Observations:                 101   AIC:                            -386.3\n",
      "Df Residuals:                      96   BIC:                            -373.2\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0485      0.009      5.502      0.000       0.031       0.066\n",
      "PC1          5.66e-09   2.63e-09      2.153      0.034    4.42e-10    1.09e-08\n",
      "PC3         2.063e-07   2.18e-08      9.455      0.000    1.63e-07     2.5e-07\n",
      "PC4         4.626e-07   4.73e-08      9.780      0.000    3.69e-07    5.57e-07\n",
      "PC5          -6.4e-07   1.09e-07     -5.870      0.000   -8.56e-07   -4.24e-07\n",
      "==============================================================================\n",
      "Omnibus:                        2.914   Durbin-Watson:                   2.053\n",
      "Prob(Omnibus):                  0.233   Jarque-Bera (JB):                2.519\n",
      "Skew:                          -0.186   Prob(JB):                        0.284\n",
      "Kurtosis:                       3.678   Cond. No.                     7.42e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.42e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CP1 = -0.29*X_1-0.76*X_2-0.02*X_3+0.01*X_4-0.02*X_5-0.11*X_6-0.53*X_7+0.15*X_8-0.15*X_9+0.0*X_10\n",
      "CP2 = -0.94*X_1+0.35*X_2+0.01*X_3+0.01*X_4+0.0*X_5-0.0*X_6+0.0*X_7+0.0*X_8-0.01*X_9-0.0*X_10\n",
      "CP4 = -0.05*X_1-0.14*X_2-0.23*X_3+0.43*X_4-0.16*X_5-0.37*X_6+0.1*X_7-0.63*X_8+0.2*X_9+0.35*X_10\n"
     ]
    }
   ],
   "source": [
    "# 导入包\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sqlalchemy import create_engine\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from matplotlib import pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pymysql\n",
    "db_config = {\n",
    "    'host': '111.231.14.211',\n",
    "    'user': 'tushare',\n",
    "    'password': 'root',\n",
    "    'database': 'tushare',\n",
    "    'port': 13307,          # 明确指定端口\n",
    "    'charset': 'utf8mb4'   # 添加字符集设置\n",
    "}\n",
    "engine = create_engine(\n",
    "    f\"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}?charset={db_config['charset']}\"\n",
    ")\n",
    "conn = pymysql.connect(**db_config)\n",
    "chunk_size = 10000\n",
    "\n",
    "# 获取华夏银行日线数据\n",
    "df = pd.read_sql_query(\n",
    "        \"\"\"\n",
    "        SELECT d.closes, d.vol , d.amount , m.buy_sm_vol , m.sell_sm_vol , m.buy_md_vol , m.sell_md_vol , m.buy_lg_vol , m.sell_lg_vol , m.buy_elg_vol , m.sell_elg_vol \n",
    "        FROM date_1 d\n",
    "        JOIN moneyflows m \n",
    "        ON d.ts_code = m.ts_code AND d.trade_date = m.trade_date\n",
    "        WHERE d.trade_date BETWEEN '2023-02-01' AND '2023-07-01' AND d.ts_code='002229.SZ'\n",
    "        \"\"\", \n",
    "        conn, \n",
    "        chunksize=chunk_size\n",
    "    )\n",
    "df1 = pd.concat(df, ignore_index=True)\n",
    "\n",
    "df1.head()\n",
    "# 增加一列，股票的涨跌幅\n",
    "df1['zd_close'] = round(((df1['closes'] - df1['closes'].shift(1)) / df1['closes'].shift(1)), 2)\n",
    "# 处理缺失值\n",
    "df1 = df1.dropna(subset=['zd_close']).reset_index(drop=True)\n",
    "\n",
    "numeric_cols = df1.select_dtypes(include=['number']).columns.tolist()\n",
    "\n",
    "# 选择需要进行主成分分析的自变量\n",
    "X = df1[['vol', 'amount', 'buy_sm_vol', 'sell_sm_vol', 'buy_md_vol', 'sell_md_vol', 'buy_lg_vol', 'sell_lg_vol', 'buy_elg_vol', 'sell_elg_vol']]\n",
    "\n",
    "# 计算特征值和特征向量\n",
    "eigenvalues, eigenvectors = np.linalg.eig(np.cov(X, rowvar=False))\n",
    "print('累计贡献率为：',round(eigenvalues[:5].sum()/eigenvalues.sum(),4)*100,'%')\n",
    "# 选择要保留的主成分个数\n",
    "n_components = 5\n",
    "top_eigenvectors = eigenvectors[:, :n_components]\n",
    "\n",
    "# 计算主成分\n",
    "principal_components = np.dot(X, top_eigenvectors)\n",
    "\n",
    "# 将主成分添加到原数据中\n",
    "principal_components = np.dot(X, top_eigenvectors)\n",
    "data_pca = pd.concat([df1, pd.DataFrame(principal_components, \n",
    "                    columns=[f'PC{i+1}' for i in range(n_components)])], axis=1)\n",
    "\n",
    "# 添加常数列前确保数据类型正确\n",
    "X_pca = data_pca[[f'PC{i+1}' for i in range(n_components)]].copy()\n",
    "X_pca = sm.add_constant(X_pca)\n",
    "\n",
    "# 确保y的索引与X_pca一致\n",
    "y = df1['zd_close'].copy()\n",
    "\n",
    "# 构建回归模型\n",
    "model_pca = sm.OLS(y, X_pca)\n",
    "\n",
    "# 拟合模型\n",
    "result_pca = model_pca.fit()\n",
    "\n",
    "# 输出结果\n",
    "print(\"\\n回归模型结果:\")\n",
    "print(result_pca.summary())\n",
    "\n",
    "# 选择PC1, PC3, PC4作为新的自变量\n",
    "X_pca_selected = data_pca[['PC1', 'PC3', 'PC4', 'PC5']]\n",
    "\n",
    "X_pca_selected.columns = ['PC1', 'PC3', 'PC4', 'PC5']\n",
    "# 添加常数列\n",
    "X_pca_selected = sm.add_constant(X_pca_selected)\n",
    "\n",
    "# 因变量\n",
    "y = df1['zd_close'].copy()\n",
    "\n",
    "# 构建回归模型\n",
    "model_pca_selected = sm.OLS(y, X_pca_selected)\n",
    "\n",
    "# 拟合模型\n",
    "result_pca_selected = model_pca_selected.fit()\n",
    "\n",
    "# 输出回归模型结果\n",
    "print(\"\\n回归模型结果:\")\n",
    "print(result_pca_selected.summary())\n",
    "\n",
    "X_pca_selected = data_pca[['PC1', 'PC3', 'PC4', 'PC5']]\n",
    "\n",
    "X_pca_selected.columns = ['PC1', 'PC3', 'PC4', 'PC5']\n",
    "# 绘制散点图\n",
    "fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(15, 5))\n",
    "\n",
    "for i, col in enumerate(X_pca_selected.columns):\n",
    "    axes[i].scatter(X_pca_selected[col], y, s=50, alpha=0.7)\n",
    "    axes[i].set_xlabel(col)\n",
    "    axes[i].set_ylabel('(y)')\n",
    "    axes[i].set_title(f'{col} ')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "for k in range(0,5):\n",
    "    string_y = f'CP{k+1} = '\n",
    "    i = eigenvectors[k]\n",
    "    for j in range(len(i)):\n",
    "        if i[j] > 0  :\n",
    "            string_y = string_y + f'+{round(i[j],2)}*X_{j+1}'\n",
    "        else:\n",
    "            string_y = string_y + f'{round(i[j],2)}*X_{j+1}'\n",
    "    if k!=2 and k!=4:\n",
    "        print(string_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1963bd3-fc1f-4b7a-a82e-d867f63ded1b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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